Principle:Allenai Open instruct Hyperparameter Search
| Knowledge Sources | |
|---|---|
| Domains | Training, Optimization |
| Last Updated | 2026-02-07 02:00 GMT |
Overview
Principle of systematically exploring hyperparameter configurations through grid search sweeps across learning rates, epochs, batch sizes, and algorithm-specific parameters to find optimal training recipes.
Description
Hyperparameter search for LLM instruction tuning involves running multiple training jobs across a grid of configurations and evaluating each on downstream benchmarks. The typical sweep covers learning rates (often spanning an order of magnitude, e.g., 1e-6 to 2e-5), training epochs (1-3), and algorithm-specific parameters (e.g., DPO beta, PPO clip ratio). For the open-instruct project, sweeps are executed by submitting parallel jobs to the Beaker cluster using mason.py, with results tracked via WandB and evaluated using the standard benchmark suite. The process is iterative: initial coarse sweeps identify promising regions, followed by refined sweeps with tighter parameter ranges.
Usage
Apply this principle when developing instruction-tuned models for new model families (OLMo, OLMoE, Qwen), when adapting training recipes across model scales (7B to 70B), or when a new training algorithm (DPO variant, GRPO) requires calibration of its specific hyperparameters.
Theoretical Basis
Grid Search:
# Abstract hyperparameter search algorithm
for lr in learning_rates:
for epochs in epoch_counts:
for seed in random_seeds:
model = train(base_model, dataset, lr=lr, epochs=epochs, seed=seed)
scores = evaluate(model, benchmarks)
record(lr, epochs, seed, scores)
best_config = select_by_metric(results, metric="average_benchmark_score")
The iterative refinement pattern:
- Coarse sweep: Wide range (e.g., LR from 1e-6 to 5e-5), few epochs, single seed
- Fine sweep: Narrow range around best (e.g., LR from 2e-6 to 1e-5), more epochs, multiple seeds
- Validation: Best config re-run with held-out evaluation set